No abstract

We study the use of stochastic discount factor (SDF) models in evaluating the investment performance of portfolio managers. By constructing artificial mutual funds with known levels of investment ability, we evaluate a large set of SDF models. We find that the measures of performance are not highly sensitive to the SDF model, and that most of the models have a mild negative bias when performance is neutral. We use the models to evaluate a sample of U.S. equity mutual funds. Adjusting for the observed bias, we find that the average mutual fund has enough ability to cover its transactions costs. Extreme funds are more likely to have good rather than poor risk adjusted performance. Our analysis also reveals a number of implementation issues relevant to other applications of SDF models.

Axon growth inhibitory proteins associated with central nervous system (CNS) myelin are responsible in part for the absence of long distance axon regeneration in the adult mammalian CNS. We have recently reported that myelin-associated glycoprotein (MAG), which is also present in peripheral nerves, is a potent inhibitor of neurite growth. This was surprising given the robust regenerative capacity of peripheral nerves. We now provide evidence that myelin purified from peripheral nerve also has neurite growth inhibitory activity. However, this activity can be masked by laminin, which is a constituent of the Schwann cell basal lamina. We also report that laminin, which is largely absent from the normal adult mammalian CNS, when added to purified CNS myelin, can override the neurite growth inhibitory activity in CNS myelin. These results have important implications for the development of strategies to foster axon regeneration in the adult mammalian CNS where multiple growth inhibitors exist.